The Internet of Vehicles (IoV) may face challenging cybersecurity attacks that may require sophisticated intrusion detection systems, necessitating a rapid development and response system. This research investigates the performance advantages of GPU-accelerated libraries (cuML) compared to traditional CPU-based implementations (scikit-learn), focusing on the speed and efficiency required for machine learning models used in IoV threat detection environments. The comprehensive evaluations conducted employ four machine learning approaches (Random Forest, KNN, Logistic Regression, XGBoost) across three distinct IoV security datasets (OTIDS, GIDS, CICIoV2024). Our findings demonstrate that GPU-accelerated implementations dramatically improved computational efficiency, with training times reduced by a factor of up to 159 and prediction speeds accelerated by up to 95 times compared to traditional CPU processing, all while preserving detection accuracy. This remarkable performance breakthrough empowers researchers and security specialists to harness GPU acceleration for creating faster, more effective threat detection systems that meet the urgent real-time security demands of today's connected vehicle networks.

本研究针对物联网车辆（IoV）所面临的复杂网络安全攻击，探讨GPU加速库（cuML）与传统CPU实现（scikit-learn）在入侵检测中的性能优势。研究表明，GPU加速显著提升计算效率，训练时间缩短至最大159倍，预测速度提升至最大95倍，同时保持检测准确性，为研究人员和安全专家提供了更快速、高效的威胁检测系统开发方案。

加速物联网入侵检测：基准测试GPU加速与基于CPU的机器学习库